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Hello all, I'd appreciate it if anyone has any references on the following or can offer their views: I am very familiar with using PCA in the usual way i.e. using a set of p correlated variables we generate a set of p uncorrelated PCs where each PC is a linear combination of these variables....we can choose to retain the first m PCs....then, say, plot these PCs' scores against each other. Now my question is....I have discussed, informally, in the past that PCs can be used also to reduce a number of variables. Say we had p potential correlated variables....we could conduct a PCA on these variables....and then, from each of the subsequent *important* PCs, choose those variables with the largest (positive or negative) coefficients.....these variables are taken as being the 'important ones' and can be used in further analysis. Does this seem OK? If so, can this technique be used when the p variables are (i) potential independent variables or (ii) potential response variables. Many thanks for your views on this, Kind Regards, Kim You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.